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Published on: June 17, 2014
Molecular design rules for wettability minimisation on grafted cellulose surfaces through human-machine teaming
Yuxiang Wang1, Tri Minh Nguyen2, Julian Berk2
1Institute for Frontier Materials, Deakin University, Geelong, VIC 3216, Australia.
Hypothesis:
Understanding how graft molecular structure controls wettability on cellulose surfaces remains a central challenge in interfacial materials design. We hypothesize that reducing wettability on grafted cellulose may depend more strongly on local branching topology and compact steric organization than on simple increases in molecular size or aromatic incorporation.
Methods:
To test this hypothesis, we combined all-atom molecular dynamics simulations of water contact angles on grafted cellulose surfaces with a human-machine teaming molecular optimisation strategy. Molecular dynamics simulations provide physically grounded wettability data for model grafted surfaces, while latent-space Bayesian optimisation can explore candidate grafts under evolving expert-defined constraints. Additional targeted perturbation studies around validated high-performing motifs examine the effects of branching density, branch position, and heavy-atom count.
Findings:
The MD simulation and optimisation results identify a narrow hydrophobic design window for grafted cellulose surfaces. Compact, densely branched aliphatic grafts consistently outperform elongated and aromatic analogues, with the best candidates yielding water contact angles above 110°. The most favorable structures are associated with limited heavy-atom counts and more localized steric bulk near the grafting point, whereas distal branching is less effective. Systematic carbon addition and deletion further show that wettability is highly sensitive to local structural perturbations, indicating that the optimal motifs occupy a narrow structural basin. These findings establish physically interpretable molecular design rules for wettability minimisation on grafted cellulose surfaces, while also providing an efficient route to identify high-performing grafts under data-sparse conditions.
